Accession Number:

ADA222310

Title:

Newton Methods for Large-Scale Linear Equality-Constrained Minimization

Descriptive Note:

Technical rept.

Corporate Author:

STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB

Personal Author(s):

Report Date:

1990-04-01

Pagination or Media Count:

34.0

Abstract:

Newton methods for large-scale minimization subject to linear equality constraints are discussed. For large-scale problems, it may be prohibitively expensive to reduce the problem to an unconstrained problem in the null space of the constraint matrix. We investigate computational schemes that enable the computation of descent directions and directions of negative curvature without the need to know the null-space matrix. The schemes are based on factorizing a sparse symmetric indefinite matrix. Three different methods are proposed for computing the desired directions. Convergence properties for the different methods are established. kr

Subject Categories:

  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE